A method to derive small area estimates of linked commuting trips by mode from open source LODES and ACS data

Credit, Kevin; Arnao, Zander

doi:10.1177/23998083221129614

Cited by 3 publications

(1 citation statement)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The way that WFH patterns have changed in response to the COVID‐19 pandemic is likely to have implications for the prosperity of cities and urban areas, requiring adaptations in planning and design to support their role in society. For example, an increase in remote working may create urban transportation challenges through modifications to typical commuting patterns, including expected declines in public transit ridership (Credit & Arnao, 2022). Furthermore, given the large body of work that has cited a decline in the economic vitality of inner cities and lower occupancy of office space during the pandemic (Rowe et al., 2023), there is further evidence to suggest their recovery is geographically uneven (Blundell et al., 2020), with ensuing implications for increasing levels of WFH in terms of the planning of residences and workplaces to provide suitable employment opportunities (Angel et al., 2022).…”

Section: Introductionmentioning

confidence: 99%

Harnessing mobility data to capture changing work from home behaviours between censuses

Gibbs,

Ballantyne,

Cheshire

et al. 2023

Geographical Journal

View full text Add to dashboard Cite

This paper provides an analysis of working from home patterns in England using data from the 2021 Census to understand (1) how patterns of working from home (WFH) in England have shifted since the COVID‐19 pandemic and (2) whether human mobility indicators, specifically Google Community Mobility Reports, provide a reliable proxy for WFH patterns recorded by the 2021 Census, providing a formal evaluation of the reliability of such datasets, whose applications have grown exponentially over the COVID‐19 pandemic. We find that WFH patterns recorded by the 2021 Census were unique compared with previous UK censuses, reflecting an unprecedented increase likely caused by persistent changes to employment during the COVID‐19 pandemic, with a clear social gradient emerging across the country. We also find that Google mobility in ‘Residential’ and ‘Workplace’ settings provides a reliable measurement of the distribution of WFH populations across Local Authorities, with varying uncertainties for mobility indicators collected in different settings. These findings provide insights into the utility of such datasets to support population research in intercensal periods, where shifts may be occurring, but can be difficult to quantify empirically.

show abstract

Section: Introductionmentioning

confidence: 99%

Harnessing mobility data to capture changing work from home behaviours between censuses

Gibbs,

Ballantyne,

Cheshire

et al. 2023

Geographical Journal

View full text Add to dashboard Cite

show abstract

Decoding the work-from-home phenomenon: insights from location-based service data

Cheung,

Chuang,

Yiu

2023

Regional Studies, Regional Science

View full text Add to dashboard Cite

DistOD: A Hybrid Privacy-Preserving and Distributed Framework for Origin–Destination Matrix Computation

Kim

2024

Electronics

View full text Add to dashboard Cite

The origin–destination (OD) matrix is a critical tool in understanding human mobility, with diverse applications. However, constructing OD matrices can pose significant privacy challenges, as sensitive information about individual mobility patterns may be exposed. In this paper, we propose DistOD, a hybrid privacy-preserving and distributed framework for the aggregation and computation of OD matrices without relying on a trusted central server. The proposed framework makes several key contributions. First, we propose a distributed method that enables multiple participating parties to collaboratively identify hotspot areas, which are regions frequently traveled between by individuals across these parties. To optimize the data utility and minimize the computational overhead, we introduce a hybrid privacy-preserving mechanism. This mechanism applies distributed differential privacy in hotspot areas to ensure high data utility, while using localized differential privacy in non-hotspot regions to reduce the computational costs. By combining these approaches, our method achieves an effective balance between computational efficiency and the accuracy of the OD matrix. Extensive experiments on real-world datasets show that DistOD consistently provides higher data utility than methods based solely on localized differential privacy, as well as greater efficiency than approaches based solely on distributed differential privacy.

show abstract

A method to derive small area estimates of linked commuting trips by mode from open source LODES and ACS data

Cited by 3 publications

References 37 publications

Harnessing mobility data to capture changing work from home behaviours between censuses

Harnessing mobility data to capture changing work from home behaviours between censuses

Decoding the work-from-home phenomenon: insights from location-based service data

DistOD: A Hybrid Privacy-Preserving and Distributed Framework for Origin–Destination Matrix Computation

Contact Info

Product

Resources

About